Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations92989
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.0 MiB
Average record size in memory192.0 B

Variable types

Text6
DateTime2
Categorical3
Numeric13

Alerts

Crawled_date has constant value "2024-12-05 00:00:00" Constant
Average_Rating is highly overall correlated with Deviation of star ratingsHigh correlation
Deviation of star ratings is highly overall correlated with Average_Rating and 1 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading EaseHigh correlation
Flesch Reading Ease is highly overall correlated with FOG IndexHigh correlation
Rating is highly overall correlated with Deviation of star ratingsHigh correlation
breadth is highly overall correlated with depth and 1 other fieldsHigh correlation
depth is highly overall correlated with breadthHigh correlation
sentiment_score_discrete is highly overall correlated with valenceHigh correlation
text_length is highly overall correlated with breadthHigh correlation
valence is highly overall correlated with sentiment_score_discreteHigh correlation
Rating is highly imbalanced (62.5%) Imbalance
Helpfulness is highly skewed (γ1 = 46.72575466) Skewed
Helpfulness has 86173 (92.7%) zeros Zeros

Reproduction

Analysis started2025-02-10 05:57:02.518369
Analysis finished2025-02-10 05:57:24.689911
Duration22.17 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct92988
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:24.876172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8958
Median length2838
Mean length242.51017
Min length6

Characters and Unicode

Total characters22550778
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92987 ?
Unique (%)> 99.9%

Sample

1st rowI enjoyed both The Martian and Artemis so I pre-ordered this one and started it immediately. It did not disappoint. Andy Weir is one of my favorite authors and Ray Porter is one of my favorite narrators so this combination is a win-win. The narration is superb and the writing is great. I recommend this book. Don't over think it. This is worth the price of admission.Disclaimer My enjoyment of the narrator is based on my listening speed. I only leave 5 stars for books I've listened to or will listen to multiple times.
2nd rowAwesome story telling. Great build up of the characters and universe. Can't compare to The Martian as that was novelunique, but this absolutely crushes Artemis. Reminds me of a cross between Old Man's War and the Three Body Problem (but slightly less cerebral than the latter).
3rd rowLet me start off by saying that I strongly enjoyed The Martian and Artemis (please, please don't let the negative comments of others dissuade you from reading Artemis). Until yesterday, American Gods was my unrivaled favorite as of finishing Project Hail Mary, it is now tied for my very favorite. I will not provide spoilers, but if you enjoy good science fiction (Scalzi, Taylor, Adams) and understand that what makes good science fiction is good science, get Project Hail Mary.As for Ray Porter, I fell in love with his narration of We Are Legion (We Are Bob) and its sequels. His enthusiastic, geeky, humorous, witty, and sarcastic tones are an absolute delight to my ears. No other narrator could have done as well or better.I don't regret preordering both the audiobook and a signed copy of Project Hail Mary in the slightest. To the contrary, I am elated and am looking forward to listening to this audiobook many, many times.
4th rowEvery once in a while I'll finish a book and can't help but get a bit depressed. Knowing that the magic and intrigue you felt can never quite be captured again. Part of this comes from completing it so quickly, I just couldn't put it down. The other was I KNEW I would love it just because it was written by Andy Weir. Most books it takes a few chapters to start getting into it but was hooked from the start.Without giving anything away I'd say that it's a mix of the Bobiverse and the Martian. The amazing adventure that comes with space while geeking out on science projects.
5th rowIn the Martian his "high school science lecture" content was acceptable because of the suspense. Here, which as far as I can tell is an attempt to re-create that, it totally fails. After several hours of boring basic science and NOTHING at all happening, I had enough. It's just dull, the attempt at suspense seems manufactured and there's no action. I really liked Artemis, and wish he's written a sequel to that. I am returning this one disappointed.
ValueCountFrequency (%)
the 207756
 
5.1%
and 139353
 
3.4%
i 127153
 
3.1%
to 118499
 
2.9%
a 104735
 
2.6%
of 89192
 
2.2%
this 81729
 
2.0%
it 77407
 
1.9%
is 60894
 
1.5%
book 60017
 
1.5%
Other values (68671) 2986287
73.7%
2025-02-10T14:57:25.203063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4013594
17.8%
e 2067891
 
9.2%
t 1657773
 
7.4%
o 1450748
 
6.4%
a 1393231
 
6.2%
i 1264925
 
5.6%
n 1192186
 
5.3%
s 1097381
 
4.9%
r 1056190
 
4.7%
h 877304
 
3.9%
Other values (67) 6479555
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22550778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4013594
17.8%
e 2067891
 
9.2%
t 1657773
 
7.4%
o 1450748
 
6.4%
a 1393231
 
6.2%
i 1264925
 
5.6%
n 1192186
 
5.3%
s 1097381
 
4.9%
r 1056190
 
4.7%
h 877304
 
3.9%
Other values (67) 6479555
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22550778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4013594
17.8%
e 2067891
 
9.2%
t 1657773
 
7.4%
o 1450748
 
6.4%
a 1393231
 
6.2%
i 1264925
 
5.6%
n 1192186
 
5.3%
s 1097381
 
4.9%
r 1056190
 
4.7%
h 877304
 
3.9%
Other values (67) 6479555
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22550778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4013594
17.8%
e 2067891
 
9.2%
t 1657773
 
7.4%
o 1450748
 
6.4%
a 1393231
 
6.2%
i 1264925
 
5.6%
n 1192186
 
5.3%
s 1097381
 
4.9%
r 1056190
 
4.7%
h 877304
 
3.9%
Other values (67) 6479555
28.7%
Distinct3857
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
Minimum2014-01-01 00:00:00
Maximum2024-12-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-10T14:57:25.296919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:25.399389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rating
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
5
78299 
4
9166 
3
 
2705
1
 
1470
2
 
1349

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters92989
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 78299
84.2%
4 9166
 
9.9%
3 2705
 
2.9%
1 1470
 
1.6%
2 1349
 
1.5%

Length

2025-02-10T14:57:25.491248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T14:57:25.559079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5 78299
84.2%
4 9166
 
9.9%
3 2705
 
2.9%
1 1470
 
1.6%
2 1349
 
1.5%

Most occurring characters

ValueCountFrequency (%)
5 78299
84.2%
4 9166
 
9.9%
3 2705
 
2.9%
1 1470
 
1.6%
2 1349
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92989
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 78299
84.2%
4 9166
 
9.9%
3 2705
 
2.9%
1 1470
 
1.6%
2 1349
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92989
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 78299
84.2%
4 9166
 
9.9%
3 2705
 
2.9%
1 1470
 
1.6%
2 1349
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92989
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 78299
84.2%
4 9166
 
9.9%
3 2705
 
2.9%
1 1470
 
1.6%
2 1349
 
1.5%

Average_Rating
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6524428
Minimum4
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:25.626265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.3
Q14.6
median4.7
Q34.8
95-th percentile4.9
Maximum4.9
Range0.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.18826323
Coefficient of variation (CV)0.040465458
Kurtosis0.58598541
Mean4.6524428
Median Absolute Deviation (MAD)0.1
Skewness-0.82488567
Sum432626
Variance0.035443042
MonotonicityNot monotonic
2025-02-10T14:57:25.697699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4.6 20713
22.3%
4.7 19586
21.1%
4.8 17439
18.8%
4.9 13965
15.0%
4.4 7328
 
7.9%
4.5 6940
 
7.5%
4.3 4335
 
4.7%
4.2 1218
 
1.3%
4 795
 
0.9%
4.1 670
 
0.7%
ValueCountFrequency (%)
4 795
 
0.9%
4.1 670
 
0.7%
4.2 1218
 
1.3%
4.3 4335
 
4.7%
4.4 7328
 
7.9%
4.5 6940
 
7.5%
4.6 20713
22.3%
4.7 19586
21.1%
4.8 17439
18.8%
4.9 13965
15.0%
ValueCountFrequency (%)
4.9 13965
15.0%
4.8 17439
18.8%
4.7 19586
21.1%
4.6 20713
22.3%
4.5 6940
 
7.5%
4.4 7328
 
7.9%
4.3 4335
 
4.7%
4.2 1218
 
1.3%
4.1 670
 
0.7%
4 795
 
0.9%

Num_of_Ratings
Real number (ℝ)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45264.291
Minimum98
Maximum215239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:25.783119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile5126
Q113701
median26734
Q352781
95-th percentile182379
Maximum215239
Range215141
Interquartile range (IQR)39080

Descriptive statistics

Standard deviation50730.698
Coefficient of variation (CV)1.1207664
Kurtosis3.8190278
Mean45264.291
Median Absolute Deviation (MAD)15975
Skewness2.1442774
Sum4.2090812 × 109
Variance2.5736037 × 109
MonotonicityNot monotonic
2025-02-10T14:57:25.881343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182379 1699
 
1.8%
54757 1664
 
1.8%
36116 1650
 
1.8%
52781 1611
 
1.7%
45875 1579
 
1.7%
181868 1579
 
1.7%
15885 1575
 
1.7%
32619 1568
 
1.7%
47362 1555
 
1.7%
101379 1553
 
1.7%
Other values (90) 76956
82.8%
ValueCountFrequency (%)
98 21
 
< 0.1%
101 24
 
< 0.1%
138 38
 
< 0.1%
180 32
 
< 0.1%
243 69
0.1%
463 53
0.1%
561 62
0.1%
1031 70
0.1%
1033 96
0.1%
1042 82
0.1%
ValueCountFrequency (%)
215239 1510
1.6%
202151 1490
1.6%
196712 1505
1.6%
182379 1699
1.8%
181868 1579
1.7%
104740 1491
1.6%
101379 1553
1.7%
89067 1457
1.6%
79190 1137
1.2%
62022 1535
1.7%

Helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct144
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45425803
Minimum0
Maximum656
Zeros86173
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:25.975364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum656
Range656
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.5008701
Coefficient of variation (CV)16.512355
Kurtosis2982.3601
Mean0.45425803
Median Absolute Deviation (MAD)0
Skewness46.725755
Sum42241
Variance56.263052
MonotonicityNot monotonic
2025-02-10T14:57:26.182631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86173
92.7%
1 4231
 
4.6%
2 929
 
1.0%
3 426
 
0.5%
4 226
 
0.2%
5 117
 
0.1%
6 93
 
0.1%
7 70
 
0.1%
8 57
 
0.1%
9 47
 
0.1%
Other values (134) 620
 
0.7%
ValueCountFrequency (%)
0 86173
92.7%
1 4231
 
4.6%
2 929
 
1.0%
3 426
 
0.5%
4 226
 
0.2%
5 117
 
0.1%
6 93
 
0.1%
7 70
 
0.1%
8 57
 
0.1%
9 47
 
0.1%
ValueCountFrequency (%)
656 1
< 0.1%
640 1
< 0.1%
597 1
< 0.1%
596 1
< 0.1%
467 1
< 0.1%
452 1
< 0.1%
429 1
< 0.1%
381 1
< 0.1%
364 1
< 0.1%
353 1
< 0.1%
Distinct62041
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:26.397791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length128
Median length113
Mean length21.599372
Min length1

Characters and Unicode

Total characters2008504
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56939 ?
Unique (%)61.2%

Sample

1st rowBazinga
2nd rowAbsolutely Great... way better than Artemis
3rd rowHighest Order of Geekgasm Medal
4th rowSo good, it's depressing
5th rowNOT the Martian
ValueCountFrequency (%)
a 11094
 
3.3%
the 9347
 
2.8%
story 8820
 
2.6%
book 8741
 
2.6%
and 8620
 
2.6%
great 8476
 
2.5%
of 5615
 
1.7%
it 5403
 
1.6%
this 5075
 
1.5%
amazing 5023
 
1.5%
Other values (12870) 258609
77.2%
2025-02-10T14:57:26.728227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
243118
 
12.1%
e 182257
 
9.1%
t 143282
 
7.1%
o 134005
 
6.7%
a 124389
 
6.2%
i 119084
 
5.9%
n 117541
 
5.9%
r 110643
 
5.5%
s 82633
 
4.1%
l 77597
 
3.9%
Other values (63) 673955
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2008504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
243118
 
12.1%
e 182257
 
9.1%
t 143282
 
7.1%
o 134005
 
6.7%
a 124389
 
6.2%
i 119084
 
5.9%
n 117541
 
5.9%
r 110643
 
5.5%
s 82633
 
4.1%
l 77597
 
3.9%
Other values (63) 673955
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2008504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
243118
 
12.1%
e 182257
 
9.1%
t 143282
 
7.1%
o 134005
 
6.7%
a 124389
 
6.2%
i 119084
 
5.9%
n 117541
 
5.9%
r 110643
 
5.5%
s 82633
 
4.1%
l 77597
 
3.9%
Other values (63) 673955
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2008504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
243118
 
12.1%
e 182257
 
9.1%
t 143282
 
7.1%
o 134005
 
6.7%
a 124389
 
6.2%
i 119084
 
5.9%
n 117541
 
5.9%
r 110643
 
5.5%
s 82633
 
4.1%
l 77597
 
3.9%
Other values (63) 673955
33.6%
Distinct105
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:26.898433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length26
Mean length22.022626
Min length10

Characters and Unicode

Total characters2047862
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row162,833,158,952,619
2nd row162,833,158,952,619
3rd row162,833,158,952,619
4th row162,833,158,952,619
5th row162,833,158,952,619
ValueCountFrequency (%)
162,833,158,952,619 1699
 
1.8%
4,298,988,032,159,490,000 1664
 
1.8%
24,398,724,826,831 1649
 
1.8%
417,416,347,248,210 1609
 
1.7%
3,821,559,221,240,270,000 1579
 
1.7%
167,358,112,932,099 1578
 
1.7%
122,412,785,628,123,000 1575
 
1.7%
2,197,371,202,395,650,000 1568
 
1.7%
4,047,950,731,181,290,000 1555
 
1.7%
264,343,372,820,249,000 1553
 
1.7%
Other values (95) 76960
82.8%
2025-02-10T14:57:27.169598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 460291
22.5%
0 285502
13.9%
1 226591
11.1%
2 171241
 
8.4%
4 157076
 
7.7%
3 145453
 
7.1%
6 132678
 
6.5%
8 127459
 
6.2%
5 121975
 
6.0%
9 116105
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2047862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 460291
22.5%
0 285502
13.9%
1 226591
11.1%
2 171241
 
8.4%
4 157076
 
7.7%
3 145453
 
7.1%
6 132678
 
6.5%
8 127459
 
6.2%
5 121975
 
6.0%
9 116105
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2047862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 460291
22.5%
0 285502
13.9%
1 226591
11.1%
2 171241
 
8.4%
4 157076
 
7.7%
3 145453
 
7.1%
6 132678
 
6.5%
8 127459
 
6.2%
5 121975
 
6.0%
9 116105
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2047862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 460291
22.5%
0 285502
13.9%
1 226591
11.1%
2 171241
 
8.4%
4 157076
 
7.7%
3 145453
 
7.1%
6 132678
 
6.5%
8 127459
 
6.2%
5 121975
 
6.0%
9 116105
 
5.7%
Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:27.343422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length23
Mean length21.913119
Min length10

Characters and Unicode

Total characters2037679
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15,673,379,891,298,200,000
2nd row15,673,379,891,298,200,000
3rd row15,673,379,891,298,200,000
4th row15,673,379,891,298,200,000
5th row15,673,379,891,298,200,000
ValueCountFrequency (%)
15,673,379,891,298,200,000 1699
 
1.8%
437,624,431,816,203,000 1664
 
1.8%
2,472,953,161,623,510,000 1650
 
1.8%
419,922,633,727,328,000 1611
 
1.7%
366,673,855,831,145,000 1579
 
1.7%
15,076,484,451,619,300,000 1579
 
1.7%
12353,1605,321,55,61 1575
 
1.7%
2,419,239,731,106,290,000 1568
 
1.7%
368,383,860,925,211,000 1555
 
1.7%
7,975,995,391,759,400,000 1553
 
1.7%
Other values (90) 76956
82.8%
2025-02-10T14:57:27.620022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 453248
22.2%
0 302484
14.8%
1 218172
10.7%
2 176468
 
8.7%
3 155727
 
7.6%
5 140917
 
6.9%
6 133280
 
6.5%
4 120455
 
5.9%
7 118164
 
5.8%
8 113146
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2037679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 453248
22.2%
0 302484
14.8%
1 218172
10.7%
2 176468
 
8.7%
3 155727
 
7.6%
5 140917
 
6.9%
6 133280
 
6.5%
4 120455
 
5.9%
7 118164
 
5.8%
8 113146
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2037679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 453248
22.2%
0 302484
14.8%
1 218172
10.7%
2 176468
 
8.7%
3 155727
 
7.6%
5 140917
 
6.9%
6 133280
 
6.5%
4 120455
 
5.9%
7 118164
 
5.8%
8 113146
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2037679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 453248
22.2%
0 302484
14.8%
1 218172
10.7%
2 176468
 
8.7%
3 155727
 
7.6%
5 140917
 
6.9%
6 133280
 
6.5%
4 120455
 
5.9%
7 118164
 
5.8%
8 113146
 
5.6%
Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:27.796672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length26
Mean length23.462237
Min length10

Characters and Unicode

Total characters2181730
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row145,112,168,722,818,000,000
2nd row145,112,168,722,818,000,000
3rd row145,112,168,722,818,000,000
4th row145,112,168,722,818,000,000
5th row145,112,168,722,818,000,000
ValueCountFrequency (%)
145,112,168,722,818,000,000 1699
 
1.8%
3,780,582,862,189,530,000 1664
 
1.8%
2,186,363,132,504,950,000 1650
 
1.8%
354,835,976,242,010,000,000 1609
 
1.7%
3,435,054,521,234,250,000 1579
 
1.7%
146,729,110,861,957,000,000 1578
 
1.7%
10595,2743,765,147,92 1575
 
1.7%
1,932,465,982,562,720,000 1568
 
1.7%
3,601,741,831,156,270,000 1555
 
1.7%
7,987,290,931,753,480,000 1553
 
1.7%
Other values (93) 76959
82.8%
2025-02-10T14:57:28.074742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 494356
22.7%
0 391710
18.0%
1 216554
9.9%
2 191736
 
8.8%
3 157443
 
7.2%
5 147863
 
6.8%
4 125666
 
5.8%
7 122455
 
5.6%
6 119612
 
5.5%
8 113662
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2181730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 494356
22.7%
0 391710
18.0%
1 216554
9.9%
2 191736
 
8.8%
3 157443
 
7.2%
5 147863
 
6.8%
4 125666
 
5.8%
7 122455
 
5.6%
6 119612
 
5.5%
8 113662
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2181730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 494356
22.7%
0 391710
18.0%
1 216554
9.9%
2 191736
 
8.8%
3 157443
 
7.2%
5 147863
 
6.8%
4 125666
 
5.8%
7 122455
 
5.6%
6 119612
 
5.5%
8 113662
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2181730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 494356
22.7%
0 391710
18.0%
1 216554
9.9%
2 191736
 
8.8%
3 157443
 
7.2%
5 147863
 
6.8%
4 125666
 
5.8%
7 122455
 
5.6%
6 119612
 
5.5%
8 113662
 
5.2%

title_length
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6007485
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:28.160205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4057432
Coefficient of variation (CV)0.66812309
Kurtosis3.4290559
Mean3.6007485
Median Absolute Deviation (MAD)1
Skewness1.395874
Sum334830
Variance5.7876004
MonotonicityNot monotonic
2025-02-10T14:57:28.244636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 21883
23.5%
1 17002
18.3%
3 14940
16.1%
4 12250
13.2%
5 8698
 
9.4%
6 6611
 
7.1%
7 4615
 
5.0%
8 3073
 
3.3%
9 1913
 
2.1%
10 997
 
1.1%
Other values (18) 1007
 
1.1%
ValueCountFrequency (%)
1 17002
18.3%
2 21883
23.5%
3 14940
16.1%
4 12250
13.2%
5 8698
 
9.4%
6 6611
 
7.1%
7 4615
 
5.0%
8 3073
 
3.3%
9 1913
 
2.1%
10 997
 
1.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 3
 
< 0.1%
24 4
 
< 0.1%
23 2
 
< 0.1%
22 5
 
< 0.1%
21 10
< 0.1%
20 19
< 0.1%
19 15
< 0.1%

text_length
Real number (ℝ)

High correlation 

Distinct525
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.589285
Minimum3
Maximum1648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:28.335802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15
Q119
median29
Q350
95-th percentile117
Maximum1648
Range1645
Interquartile range (IQR)31

Descriptive statistics

Standard deviation46.960846
Coefficient of variation (CV)1.0773484
Kurtosis94.867364
Mean43.589285
Median Absolute Deviation (MAD)12
Skewness6.5198575
Sum4053324
Variance2205.3211
MonotonicityNot monotonic
2025-02-10T14:57:28.433763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 7126
 
7.7%
16 4913
 
5.3%
17 4082
 
4.4%
18 3768
 
4.1%
19 3396
 
3.7%
20 3170
 
3.4%
21 2864
 
3.1%
22 2608
 
2.8%
23 2376
 
2.6%
24 2211
 
2.4%
Other values (515) 56475
60.7%
ValueCountFrequency (%)
3 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 4
 
< 0.1%
11 11
 
< 0.1%
12 31
 
< 0.1%
13 183
 
0.2%
14 1324
 
1.4%
15 7126
7.7%
ValueCountFrequency (%)
1648 1
< 0.1%
1592 1
< 0.1%
1400 1
< 0.1%
1194 1
< 0.1%
1121 1
< 0.1%
1114 1
< 0.1%
1112 1
< 0.1%
1092 1
< 0.1%
1078 1
< 0.1%
1021 1
< 0.1%

Crawled_date
Date

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
Minimum2024-12-05 00:00:00
Maximum2024-12-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-10T14:57:28.511809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:28.575114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

time_lapsed
Real number (ℝ)

Distinct3857
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1642.963
Minimum1
Maximum3991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:28.660535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile276
Q1865
median1591
Q32311
95-th percentile3216
Maximum3991
Range3990
Interquartile range (IQR)1446

Descriptive statistics

Standard deviation907.60457
Coefficient of variation (CV)0.55241937
Kurtosis-0.85828006
Mean1642.963
Median Absolute Deviation (MAD)721
Skewness0.21747945
Sum1.5277748 × 108
Variance823746.05
MonotonicityNot monotonic
2025-02-10T14:57:28.764007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2313 417
 
0.4%
2312 141
 
0.2%
2278 91
 
0.1%
685 88
 
0.1%
2579 83
 
0.1%
693 82
 
0.1%
2277 81
 
0.1%
680 80
 
0.1%
687 79
 
0.1%
675 79
 
0.1%
Other values (3847) 91768
98.7%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 6
 
< 0.1%
3 14
< 0.1%
4 15
< 0.1%
5 15
< 0.1%
6 8
< 0.1%
7 10
< 0.1%
8 14
< 0.1%
9 18
< 0.1%
10 18
< 0.1%
ValueCountFrequency (%)
3991 2
< 0.1%
3990 4
< 0.1%
3989 2
< 0.1%
3986 1
 
< 0.1%
3985 2
< 0.1%
3984 1
 
< 0.1%
3982 2
< 0.1%
3980 1
 
< 0.1%
3978 2
< 0.1%
3976 1
 
< 0.1%

Deviation of star ratings
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46146749
Minimum0
Maximum3.9
Zeros73
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:28.862274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.3
Q30.5
95-th percentile1.4
Maximum3.9
Range3.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.52016655
Coefficient of variation (CV)1.1272009
Kurtosis17.506504
Mean0.46146749
Median Absolute Deviation (MAD)0.1
Skewness3.8954927
Sum42911.4
Variance0.27057324
MonotonicityNot monotonic
2025-02-10T14:57:28.962719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.4 18624
20.0%
0.3 18036
19.4%
0.2 16368
17.6%
0.1 13406
14.4%
0.6 7167
 
7.7%
0.5 5989
 
6.4%
0.7 4589
 
4.9%
0.8 1909
 
2.1%
0.9 950
 
1.0%
1.4 866
 
0.9%
Other values (30) 5085
 
5.5%
ValueCountFrequency (%)
0 73
 
0.1%
0.1 13406
14.4%
0.2 16368
17.6%
0.3 18036
19.4%
0.4 18624
20.0%
0.5 5989
 
6.4%
0.6 7167
 
7.7%
0.7 4589
 
4.9%
0.8 1909
 
2.1%
0.9 950
 
1.0%
ValueCountFrequency (%)
3.9 19
 
< 0.1%
3.8 42
 
< 0.1%
3.7 97
 
0.1%
3.6 261
0.3%
3.5 298
0.3%
3.4 206
0.2%
3.3 255
0.3%
3.2 54
 
0.1%
3.1 53
 
0.1%
3 185
0.2%

FOG Index
Real number (ℝ)

High correlation 

Distinct1814
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5832787
Minimum1.2
Maximum108.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:29.066331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile3.6
Q16.31
median8.2
Q310.39
95-th percentile14.11
Maximum108.15
Range106.95
Interquartile range (IQR)4.08

Descriptive statistics

Standard deviation3.3393678
Coefficient of variation (CV)0.38905504
Kurtosis17.708702
Mean8.5832787
Median Absolute Deviation (MAD)2.07
Skewness1.6020841
Sum798150.5
Variance11.151377
MonotonicityNot monotonic
2025-02-10T14:57:29.167305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 1374
 
1.5%
8.04 1329
 
1.4%
5.67 1221
 
1.3%
8.2 1166
 
1.3%
8.01 1143
 
1.2%
5.7 1025
 
1.1%
8.33 1013
 
1.1%
8.67 971
 
1.0%
10 967
 
1.0%
8 835
 
0.9%
Other values (1804) 81945
88.1%
ValueCountFrequency (%)
1.2 1
 
< 0.1%
1.32 1
 
< 0.1%
1.4 4
 
< 0.1%
1.52 16
< 0.1%
1.6 12
< 0.1%
1.68 1
 
< 0.1%
1.72 28
< 0.1%
1.76 2
 
< 0.1%
1.8 28
< 0.1%
1.84 1
 
< 0.1%
ValueCountFrequency (%)
108.15 1
< 0.1%
80.1 1
< 0.1%
64.84 1
< 0.1%
62.28 1
< 0.1%
55 1
< 0.1%
53.71 1
< 0.1%
49.46 1
< 0.1%
47.83 1
< 0.1%
47.35 1
< 0.1%
45.91 1
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation 

Distinct2186
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.527757
Minimum-189.44
Maximum117.87
Zeros0
Zeros (%)0.0%
Negative71
Negative (%)0.1%
Memory size726.6 KiB
2025-02-10T14:57:29.260446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-189.44
5-th percentile46.44
Q164.41
median74.56
Q383.86
95-th percentile96.89
Maximum117.87
Range307.31
Interquartile range (IQR)19.45

Descriptive statistics

Standard deviation15.603532
Coefficient of variation (CV)0.21221281
Kurtosis3.1211518
Mean73.527757
Median Absolute Deviation (MAD)9.85
Skewness-0.76022411
Sum6837272.6
Variance243.47022
MonotonicityNot monotonic
2025-02-10T14:57:29.358771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.28 870
 
0.9%
89.24 822
 
0.9%
72.32 820
 
0.9%
81.63 809
 
0.9%
88.74 768
 
0.8%
79.26 741
 
0.8%
78.25 679
 
0.7%
64.71 662
 
0.7%
87.72 645
 
0.7%
73.17 595
 
0.6%
Other values (2176) 85578
92.0%
ValueCountFrequency (%)
-189.44 1
< 0.1%
-151.82 1
< 0.1%
-107.51 1
< 0.1%
-70.66 1
< 0.1%
-62.85 1
< 0.1%
-60.48 1
< 0.1%
-58.14 1
< 0.1%
-54.75 1
< 0.1%
-52.05 1
< 0.1%
-46.61 1
< 0.1%
ValueCountFrequency (%)
117.87 2
 
< 0.1%
117.46 4
 
< 0.1%
117.16 7
< 0.1%
116.86 6
< 0.1%
116.65 1
 
< 0.1%
116.45 3
 
< 0.1%
116.15 3
 
< 0.1%
115.84 2
 
< 0.1%
115.43 1
 
< 0.1%
115.13 10
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct92770
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53479273
Minimum8.51 × 10-18
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:29.458495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8.51 × 10-18
5-th percentile0.24093305
Q10.4280957
median0.54591683
Q30.65823431
95-th percentile0.79691921
Maximum1
Range1
Interquartile range (IQR)0.23013861

Descriptive statistics

Standard deviation0.17183919
Coefficient of variation (CV)0.32131922
Kurtosis0.080695336
Mean0.53479273
Median Absolute Deviation (MAD)0.11519185
Skewness-0.40502448
Sum49729.841
Variance0.029528706
MonotonicityNot monotonic
2025-02-10T14:57:29.684791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57
 
0.1%
2.54 × 10-175
 
< 0.1%
3.43 × 10-175
 
< 0.1%
1.55 × 10-175
 
< 0.1%
2.81 × 10-174
 
< 0.1%
2.45 × 10-174
 
< 0.1%
1.79 × 10-174
 
< 0.1%
1.05 × 10-174
 
< 0.1%
2.53 × 10-173
 
< 0.1%
0.25565608 3
 
< 0.1%
Other values (92760) 92895
99.9%
ValueCountFrequency (%)
8.51 × 10-181
 
< 0.1%
8.73 × 10-183
< 0.1%
8.87 × 10-181
 
< 0.1%
9.51 × 10-181
 
< 0.1%
1.01 × 10-171
 
< 0.1%
1.05 × 10-174
< 0.1%
1.06 × 10-171
 
< 0.1%
1.07 × 10-171
 
< 0.1%
1.08 × 10-171
 
< 0.1%
1.1 × 10-171
 
< 0.1%
ValueCountFrequency (%)
1 57
0.1%
0.961395012 1
 
< 0.1%
0.95849121 1
 
< 0.1%
0.957174074 1
 
< 0.1%
0.953965845 1
 
< 0.1%
0.953522322 1
 
< 0.1%
0.953068588 1
 
< 0.1%
0.952707638 1
 
< 0.1%
0.952244768 1
 
< 0.1%
0.949793002 1
 
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct92843
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60059725
Minimum0.021903437
Maximum1.5167448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:29.779110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.021903437
5-th percentile0.29017189
Q10.45864804
median0.59121045
Q30.72844233
95-th percentile0.94631741
Maximum1.5167448
Range1.4948414
Interquartile range (IQR)0.26979429

Descriptive statistics

Standard deviation0.19979434
Coefficient of variation (CV)0.33265943
Kurtosis0.12495174
Mean0.60059725
Median Absolute Deviation (MAD)0.1347201
Skewness0.34394592
Sum55848.937
Variance0.039917777
MonotonicityNot monotonic
2025-02-10T14:57:29.880178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.021903437 57
 
0.1%
1.098597581 22
 
< 0.1%
1.099337004 21
 
< 0.1%
0.999376155 7
 
< 0.1%
1.325381402 6
 
< 0.1%
1.403972163 3
 
< 0.1%
0.952941942 3
 
< 0.1%
1.074000027 3
 
< 0.1%
0.459234305 3
 
< 0.1%
1.363796457 3
 
< 0.1%
Other values (92833) 92861
99.9%
ValueCountFrequency (%)
0.021903437 57
0.1%
0.062416632 1
 
< 0.1%
0.063770838 1
 
< 0.1%
0.06412897 1
 
< 0.1%
0.076081116 1
 
< 0.1%
0.077904459 1
 
< 0.1%
0.083334964 1
 
< 0.1%
0.087664931 1
 
< 0.1%
0.091446905 1
 
< 0.1%
0.091515224 1
 
< 0.1%
ValueCountFrequency (%)
1.516744795 2
< 0.1%
1.490205685 2
< 0.1%
1.488695538 1
< 0.1%
1.445364968 1
< 0.1%
1.444545245 1
< 0.1%
1.442130485 2
< 0.1%
1.440677351 1
< 0.1%
1.437410757 1
< 0.1%
1.435786638 1
< 0.1%
1.434969534 1
< 0.1%

valence
Real number (ℝ)

High correlation 

Distinct3867
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.211546
Minimum1.022
Maximum4.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:29.982297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.022
5-th percentile2.311
Q13.947
median4.525
Q34.793
95-th percentile4.932
Maximum4.991
Range3.969
Interquartile range (IQR)0.846

Descriptive statistics

Standard deviation0.81808815
Coefficient of variation (CV)0.19424889
Kurtosis1.9944459
Mean4.211546
Median Absolute Deviation (MAD)0.329
Skewness-1.6014942
Sum391627.45
Variance0.66926823
MonotonicityNot monotonic
2025-02-10T14:57:30.092920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.92 160
 
0.2%
4.87 159
 
0.2%
4.854 157
 
0.2%
4.834 157
 
0.2%
4.853 157
 
0.2%
4.887 156
 
0.2%
4.877 155
 
0.2%
4.922 155
 
0.2%
4.932 155
 
0.2%
4.912 152
 
0.2%
Other values (3857) 91426
98.3%
ValueCountFrequency (%)
1.022 2
< 0.1%
1.023 1
< 0.1%
1.024 2
< 0.1%
1.03 1
< 0.1%
1.031 2
< 0.1%
1.032 1
< 0.1%
1.033 1
< 0.1%
1.034 1
< 0.1%
1.035 1
< 0.1%
1.036 1
< 0.1%
ValueCountFrequency (%)
4.991 1
 
< 0.1%
4.99 5
 
< 0.1%
4.989 7
 
< 0.1%
4.988 11
< 0.1%
4.987 4
 
< 0.1%
4.986 9
< 0.1%
4.985 10
< 0.1%
4.984 9
< 0.1%
4.983 16
< 0.1%
4.982 22
< 0.1%

sentiment_score_discrete
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
5.0
59584 
4.0
20008 
3.0
6316 
2.0
 
4729
1.0
 
2352

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters278967
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row4.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
5.0 59584
64.1%
4.0 20008
 
21.5%
3.0 6316
 
6.8%
2.0 4729
 
5.1%
1.0 2352
 
2.5%

Length

2025-02-10T14:57:30.186845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T14:57:30.254701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5.0 59584
64.1%
4.0 20008
 
21.5%
3.0 6316
 
6.8%
2.0 4729
 
5.1%
1.0 2352
 
2.5%

Most occurring characters

ValueCountFrequency (%)
. 92989
33.3%
0 92989
33.3%
5 59584
21.4%
4 20008
 
7.2%
3 6316
 
2.3%
2 4729
 
1.7%
1 2352
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 278967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 92989
33.3%
0 92989
33.3%
5 59584
21.4%
4 20008
 
7.2%
3 6316
 
2.3%
2 4729
 
1.7%
1 2352
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 278967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 92989
33.3%
0 92989
33.3%
5 59584
21.4%
4 20008
 
7.2%
3 6316
 
2.3%
2 4729
 
1.7%
1 2352
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 278967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 92989
33.3%
0 92989
33.3%
5 59584
21.4%
4 20008
 
7.2%
3 6316
 
2.3%
2 4729
 
1.7%
1 2352
 
0.8%

arousal
Real number (ℝ)

Distinct92982
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13242249
Minimum-0.58002542
Maximum0.61702641
Zeros0
Zeros (%)0.0%
Negative12933
Negative (%)13.9%
Memory size726.6 KiB
2025-02-10T14:57:30.338750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-0.58002542
5-th percentile-0.15565574
Q10.06495466
median0.12600652
Q30.21559249
95-th percentile0.41346221
Maximum0.61702641
Range1.1970518
Interquartile range (IQR)0.15063783

Descriptive statistics

Standard deviation0.1681303
Coefficient of variation (CV)1.2696506
Kurtosis2.0864056
Mean0.13242249
Median Absolute Deviation (MAD)0.070966623
Skewness-0.56305524
Sum12313.835
Variance0.028267797
MonotonicityNot monotonic
2025-02-10T14:57:30.440754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.118232413 2
 
< 0.1%
0.130577476 2
 
< 0.1%
0.10631168 2
 
< 0.1%
0.073769297 2
 
< 0.1%
0.143086224 2
 
< 0.1%
0.072697214 2
 
< 0.1%
-0.054708807 2
 
< 0.1%
-0.438818729 1
 
< 0.1%
0.169543441 1
 
< 0.1%
0.054081932 1
 
< 0.1%
Other values (92972) 92972
> 99.9%
ValueCountFrequency (%)
-0.58002542 1
< 0.1%
-0.579787975 1
< 0.1%
-0.579336895 1
< 0.1%
-0.579109293 1
< 0.1%
-0.578750802 1
< 0.1%
-0.578562792 1
< 0.1%
-0.578426927 1
< 0.1%
-0.577783826 1
< 0.1%
-0.577528091 1
< 0.1%
-0.577519447 1
< 0.1%
ValueCountFrequency (%)
0.617026407 1
< 0.1%
0.613814064 1
< 0.1%
0.611316268 1
< 0.1%
0.611215104 1
< 0.1%
0.610655573 1
< 0.1%
0.609917297 1
< 0.1%
0.609443585 1
< 0.1%
0.609285699 1
< 0.1%
0.608783849 1
< 0.1%
0.608149507 1
< 0.1%
Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
2025-02-10T14:57:30.663824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length31
Mean length17.623902
Min length2

Characters and Unicode

Total characters1638829
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProject Hail Mary
2nd rowProject Hail Mary
3rd rowProject Hail Mary
4th rowProject Hail Mary
5th rowProject Hail Mary
ValueCountFrequency (%)
the 40732
 
13.4%
of 9030
 
3.0%
and 6302
 
2.1%
a 6059
 
2.0%
me 5852
 
1.9%
to 5319
 
1.7%
edition 4160
 
1.4%
in 4142
 
1.4%
man 3363
 
1.1%
are 2982
 
1.0%
Other values (220) 216519
71.1%
2025-02-10T14:57:30.984684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
211471
 
12.9%
e 182216
 
11.1%
i 105905
 
6.5%
a 95205
 
5.8%
o 87054
 
5.3%
n 86465
 
5.3%
r 82420
 
5.0%
t 78260
 
4.8%
h 70549
 
4.3%
l 56764
 
3.5%
Other values (45) 582520
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1638829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
211471
 
12.9%
e 182216
 
11.1%
i 105905
 
6.5%
a 95205
 
5.8%
o 87054
 
5.3%
n 86465
 
5.3%
r 82420
 
5.0%
t 78260
 
4.8%
h 70549
 
4.3%
l 56764
 
3.5%
Other values (45) 582520
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1638829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
211471
 
12.9%
e 182216
 
11.1%
i 105905
 
6.5%
a 95205
 
5.8%
o 87054
 
5.3%
n 86465
 
5.3%
r 82420
 
5.0%
t 78260
 
4.8%
h 70549
 
4.3%
l 56764
 
3.5%
Other values (45) 582520
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1638829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
211471
 
12.9%
e 182216
 
11.1%
i 105905
 
6.5%
a 95205
 
5.8%
o 87054
 
5.3%
n 86465
 
5.3%
r 82420
 
5.0%
t 78260
 
4.8%
h 70549
 
4.3%
l 56764
 
3.5%
Other values (45) 582520
35.5%

Categories
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size726.6 KiB
Literature & Fiction
37173 
Biographies & Memoirs
15737 
Science Fiction & Fantasy
7490 
History
6331 
Teen & Young Adult
4312 
Other values (10)
21946 

Length

Max length47
Median length28
Mean length19.788158
Min length6

Characters and Unicode

Total characters1840081
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScience Fiction & Fantasy
2nd rowScience Fiction & Fantasy
3rd rowScience Fiction & Fantasy
4th rowScience Fiction & Fantasy
5th rowScience Fiction & Fantasy

Common Values

ValueCountFrequency (%)
Literature & Fiction 37173
40.0%
Biographies & Memoirs 15737
16.9%
Science Fiction & Fantasy 7490
 
8.1%
History 6331
 
6.8%
Teen & Young Adult 4312
 
4.6%
Romance 4154
 
4.5%
Relationships, Parenting & Personal Development 3836
 
4.1%
Children's Audiobooks 3684
 
4.0%
LGBTQ+ 2583
 
2.8%
Mystery, Thriller & Suspense 2409
 
2.6%
Other values (5) 5280
 
5.7%

Length

2025-02-10T14:57:31.074069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
75215
27.8%
fiction 44663
16.5%
literature 37173
13.7%
biographies 15737
 
5.8%
memoirs 15737
 
5.8%
science 7490
 
2.8%
fantasy 7490
 
2.8%
history 6331
 
2.3%
young 4312
 
1.6%
adult 4312
 
1.6%
Other values (23) 52570
19.4%

Most occurring characters

ValueCountFrequency (%)
i 212241
11.5%
e 181356
 
9.9%
178041
 
9.7%
t 153448
 
8.3%
r 136028
 
7.4%
o 119758
 
6.5%
n 101286
 
5.5%
a 87322
 
4.7%
s 80144
 
4.4%
& 75215
 
4.1%
Other values (33) 515242
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1840081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 212241
11.5%
e 181356
 
9.9%
178041
 
9.7%
t 153448
 
8.3%
r 136028
 
7.4%
o 119758
 
6.5%
n 101286
 
5.5%
a 87322
 
4.7%
s 80144
 
4.4%
& 75215
 
4.1%
Other values (33) 515242
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1840081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 212241
11.5%
e 181356
 
9.9%
178041
 
9.7%
t 153448
 
8.3%
r 136028
 
7.4%
o 119758
 
6.5%
n 101286
 
5.5%
a 87322
 
4.7%
s 80144
 
4.4%
& 75215
 
4.1%
Other values (33) 515242
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1840081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 212241
11.5%
e 181356
 
9.9%
178041
 
9.7%
t 153448
 
8.3%
r 136028
 
7.4%
o 119758
 
6.5%
n 101286
 
5.5%
a 87322
 
4.7%
s 80144
 
4.4%
& 75215
 
4.1%
Other values (33) 515242
28.0%

Interactions

2025-02-10T14:57:22.833126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.364056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.326988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.401187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.396453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.399321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.401182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.563387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.658658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.642084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.769982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.770828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.788485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.021191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.431612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.398335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.472253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.467795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.472853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.477451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.638706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.728559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.716794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.853998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.845171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.863996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.092714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.501282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.465278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.545600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.540932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.544179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.551213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.715145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.799738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.789048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.925846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.918459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.939262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.170114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.574141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.540482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.621394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.617301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.621948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.747396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.793877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.874286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.864681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.001263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.997367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.020867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.247343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.648565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.614888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.696887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.692982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.696920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.826341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.876326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.950258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.943966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.078127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.076369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.099361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.323369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.721897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.689940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.772921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.769882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.772317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.906538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.955721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.024380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.020538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.153527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.152309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.180202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.406081image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.803183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.768754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.854118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.852090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.853299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.990778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.042096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.104724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.105494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.235277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.236146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.266362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.487232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.883967image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.850948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.937894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.933738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.937363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.077729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.168587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.185622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.189798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.315792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.319016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.351923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.561424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:10.955185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.921439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.010973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.007988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.010667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.154855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.247893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.259680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.263470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.389442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.396348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.428921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.637792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.027933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.099793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.086829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.084116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.087429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.235087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.327577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.336331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.339863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.464962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.474252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.510662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.711913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.100387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.169749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.159390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.158521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.161468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.312257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.406678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.408944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.528816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.538643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.549740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.587419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.791569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.175468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.247039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.238429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.237862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.240520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.394233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.490749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.485883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.609878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.614729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.628148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.669756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:23.876069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:11.254543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:12.326511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:13.319646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:14.321741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:15.324388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:16.482815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:17.575353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:18.568349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:19.692065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:20.696251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:21.712281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-10T14:57:22.753882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-02-10T14:57:31.139835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Average_RatingCategoriesDeviation of star ratingsFOG IndexFlesch Reading EaseHelpfulnessNum_of_RatingsRatingarousalbreadthdepthsentiment_score_discretetext_lengthtime_lapsedtitle_lengthvalence
Average_Rating1.0000.365-0.841-0.0160.035-0.0800.3370.191-0.0700.059-0.0360.148-0.035-0.166-0.0150.240
Categories0.3651.0000.1750.0300.0320.0000.4960.1160.0850.0260.0500.1050.0050.1550.0180.066
Deviation of star ratings-0.8410.1751.0000.025-0.0380.115-0.3080.8660.051-0.0690.0460.3400.0670.1290.040-0.336
FOG Index-0.0160.0300.0251.000-0.7710.020-0.0510.005-0.041-0.1900.1500.0270.238-0.0060.088-0.053
Flesch Reading Ease0.0350.032-0.038-0.7711.000-0.0170.0660.024-0.0150.157-0.0840.044-0.0730.010-0.040-0.012
Helpfulness-0.0800.0000.1150.020-0.0171.000-0.0760.012-0.003-0.0620.0520.0140.0980.1330.035-0.098
Num_of_Ratings0.3370.496-0.308-0.0510.066-0.0761.0000.0840.0230.049-0.0010.063-0.0510.155-0.0090.112
Rating0.1910.1160.8660.0050.0240.0120.0841.0000.0630.0450.0330.3740.0370.0480.0360.410
arousal-0.0700.0850.051-0.041-0.015-0.0030.0230.0631.000-0.0380.1010.140-0.026-0.025-0.0460.076
breadth0.0590.026-0.069-0.1900.157-0.0620.0490.045-0.0381.000-0.5180.073-0.533-0.027-0.1320.132
depth-0.0360.0500.0460.150-0.0840.052-0.0010.0330.101-0.5181.0000.0520.4160.0140.087-0.044
sentiment_score_discrete0.1480.1050.3400.0270.0440.0140.0630.3740.1400.0730.0521.0000.0540.0300.0460.755
text_length-0.0350.0050.0670.238-0.0730.098-0.0510.037-0.026-0.5330.4160.0541.0000.0620.216-0.190
time_lapsed-0.1660.1550.129-0.0060.0100.1330.1550.048-0.025-0.0270.0140.0300.0621.0000.029-0.027
title_length-0.0150.0180.0400.088-0.0400.035-0.0090.036-0.046-0.1320.0870.0460.2160.0291.000-0.126
valence0.2400.066-0.336-0.053-0.012-0.0980.1120.4100.0760.132-0.0440.755-0.190-0.027-0.1261.000

Missing values

2025-02-10T14:57:24.024342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-10T14:57:24.356239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Review_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessreview_titleRating_DistributionRating_of_PerformanceRating_of_Storytitle_lengthtext_lengthCrawled_datetime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousalProduct_NameCategories
0I enjoyed both The Martian and Artemis so I pre-ordered this one and started it immediately. It did not disappoint. Andy Weir is one of my favorite authors and Ray Porter is one of my favorite narrators so this combination is a win-win. The narration is superb and the writing is great. I recommend this book. Don't over think it. This is worth the price of admission.Disclaimer My enjoyment of the narrator is based on my listening speed. I only leave 5 stars for books I've listened to or will listen to multiple times.2021-05-0454.9182379656Bazinga162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,0001942024-12-051311.00.18.8477.840.8664920.3441144.0664.00.277619Project Hail MaryScience Fiction & Fantasy
1Awesome story telling. Great build up of the characters and universe. Can't compare to The Martian as that was novelunique, but this absolutely crushes Artemis. Reminds me of a cross between Old Man's War and the Three Body Problem (but slightly less cerebral than the latter).2021-05-0554.9182379159Absolutely Great... way better than Artemis162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,0006462024-12-051310.00.17.2176.720.5982870.3401554.7345.00.126615Project Hail MaryScience Fiction & Fantasy
2Let me start off by saying that I strongly enjoyed The Martian and Artemis (please, please don't let the negative comments of others dissuade you from reading Artemis). Until yesterday, American Gods was my unrivaled favorite as of finishing Project Hail Mary, it is now tied for my very favorite. I will not provide spoilers, but if you enjoy good science fiction (Scalzi, Taylor, Adams) and understand that what makes good science fiction is good science, get Project Hail Mary.As for Ray Porter, I fell in love with his narration of We Are Legion (We Are Bob) and its sequels. His enthusiastic, geeky, humorous, witty, and sarcastic tones are an absolute delight to my ears. No other narrator could have done as well or better.I don't regret preordering both the audiobook and a signed copy of Project Hail Mary in the slightest. To the contrary, I am elated and am looking forward to listening to this audiobook many, many times.2021-05-0554.9182379157Highest Order of Geekgasm Medal162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,00051602024-12-051310.00.111.0068.100.8106580.3655053.5464.00.225439Project Hail MaryScience Fiction & Fantasy
3Every once in a while I'll finish a book and can't help but get a bit depressed. Knowing that the magic and intrigue you felt can never quite be captured again. Part of this comes from completing it so quickly, I just couldn't put it down. The other was I KNEW I would love it just because it was written by Andy Weir. Most books it takes a few chapters to start getting into it but was hooked from the start.Without giving anything away I'd say that it's a mix of the Bobiverse and the Martian. The amazing adventure that comes with space while geeking out on science projects.2021-05-1154.918237934So good, it's depressing162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,00041092024-12-051304.00.16.9781.020.8208510.4626773.1773.00.023581Project Hail MaryScience Fiction & Fantasy
4In the Martian his "high school science lecture" content was acceptable because of the suspense. Here, which as far as I can tell is an attempt to re-create that, it totally fails. After several hours of boring basic science and NOTHING at all happening, I had enough. It's just dull, the attempt at suspense seems manufactured and there's no action. I really liked Artemis, and wish he's written a sequel to that. I am returning this one disappointed.2021-05-0844.918237920NOT the Martian162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,0003782024-12-051307.00.98.2866.740.6748640.4751831.5451.00.066626Project Hail MaryScience Fiction & Fantasy
5I rarely write reviews but had to this time. This was the best and most fun Ive had listening to an audiobook ever. Perfect mix of science fiction and humor and just overall amazing. The narrator is so talented! Highly highly highly recommend.2021-06-2554.918237915Wow. AMAZE. AMAZE. AMAZE.162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,0004432024-12-051259.00.18.0971.210.5708780.5828234.9525.00.310703Project Hail MaryScience Fiction & Fantasy
6It has all the science we love from an Andy Weir book. It makes you think and even look up stuff you didnt know, but it was a tad predictable. If you love his other two books you will enjoy this readlisten. Would have like to hear R.C. Bray as the narrator, the Martian audio book will always be one of my favorites. While the narrator wasnt bad in this one, just wasnt sucked into the characters as much as with the Martian or even Artemis. Maybe having preorder the book built up a hype He couldnt have possible matched but I did finish the book in three days. I would recommend.2021-05-1244.91823797Smart but predictable162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,00031122024-12-051303.00.97.0382.650.7615880.5214623.6504.00.042911Project Hail MaryScience Fiction & Fantasy
7if you know Andy Weir, this book will fit your expectations while still offering great surprises. however, it also shows done of his weakness. characters are generally flat, and the protagonist's shallow sarcasm wears on you. The reader performs it admirably, but the work could have benefited from backing off a bit rather than leaning into the goofiness.The inherently mysterious nature of the work makes it hard to discuss the plot or characters in a review, but suffice it to say that the incredible amount of research and thought that went into the science is on display, and there's some creative thinking there that seems both wondrous and plausible. All the moving parts cone together on the end, but not without reasonable yet unexpected consequences.I enjoyed it, but of you aren't a fan of Weir, try The Martian first.2021-05-1244.91823796A fun Weir novel, but a step back162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,00081392024-12-051303.00.911.9959.740.7984770.1804962.8683.00.337342Project Hail MaryScience Fiction & Fantasy
8Definately Weir returning to his origins. A single engineerscientist against all odds solving problems and making things work.It's a good book. Though the lack of interest in using the astrophage problem to solve it did become a bit annoying. They have a 5000,000 ISP rocket engine and that's never used to deploy anti astrophage methods? makes no sense.This does suffer from the Portal 2 dillema. Is it as good as the Martian? No. Is it good? Yes. Below the Martian and Artemis, but still very readable.I recommend reading. It's a bit too long though.2021-05-1144.91823793Good book, similar to the Martian, but not quite162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,0009942024-12-051304.00.95.6780.480.6035380.6629623.2123.00.150167Project Hail MaryScience Fiction & Fantasy
9I loved this story. It is laugh out loud funny, full of great science and can bring you to tears. Dont miss this wonderful story. Ray Porters narration of Project Hail Mary is spot on!2021-06-2154.91823792Happy, happy, happy!162,833,158,952,61915,673,379,891,298,200,000145,112,168,722,818,000,0003352024-12-051263.00.14.6696.380.5191270.5489414.7175.00.234205Project Hail MaryScience Fiction & Fantasy
Review_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessreview_titleRating_DistributionRating_of_PerformanceRating_of_Storytitle_lengthtext_lengthCrawled_datetime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousalProduct_NameCategories
92979I've always loved reading these types of stories. The narrator was as exceptional as the original content. I could barely stop listening.2022-01-2254.8415100This is truly a historical classic363,863,643,946,279,000327,783,374,791,206,000327,433,059,898,260,0006222024-12-051048.00.210.1964.070.7073410.6971014.7545.00.102278Pride and PrejudiceLiterature & Fiction
92980I will definitely listen again! I love how the author integrated modern and traditional Native culture and history, especially the language and customs of the Anishinaabe.2021-07-2454.768650Great story! Awesomely narrated!5424,1068,266,71,365137,747,178,45,274824,936,262,55,354262024-12-051230.00.312.8932.900.5610870.5964974.6955.00.050912Firekeepers DaughterTeen & Young Adult
92981This book is now one of my favorites. Definitely getting a hard copy. loved it.2021-01-1354.699190Couldn't stop Listening72,861,802,574,157,1007472,1148,331,54,4565,071,647,602,178,1003152024-12-051422.00.45.6789.240.4264990.7088004.9055.00.065740Aristotle and Dante Discover the Secrets of the UniverseChildren's Audiobooks
92982I loved this podcast! It was a great adventure with a great ending and so many different messages and ideas to think about )2021-04-1354.530300Really entertaining2119,536,200,98,772329,355,111,47,521903,561,208,110,932242024-12-051332.00.54.6085.180.4375160.7686864.8235.00.134809The Sea in the SkyNo categories info
92983It seems like approaching this book required I have an open mind the author himself did not have2024-07-2244.7560330Fine but nothing special4,504,278,481,980,630,0003,988,862,121,299,260,0003,782,768,361,827,530,0004182024-12-05136.00.79.4278.590.5125080.6638222.9123.0-0.440790SapiensHistory
92984Took me awhile to finish but I was not disappointed in any way at all2019-03-2054.91818680Great book Mrs. Obama!167,358,112,932,099,000,00015,076,484,451,619,300,000146,729,110,861,9574152024-12-052087.00.18.6781.630.6668220.3015983.4783.0-0.222355BecomingBiographies & Memoirs
92985Beautifully written and narrated. Hard to put the book down,and the story lingers with you well after you've read it. Painful, touching, and eye-opening all at once.2019-03-2254.6167070Haunting and compelling1,185,534,901,003,220,00010134,2688,679,117,81103,382,418,723,157,0003272024-12-052085.00.46.5670.800.4872640.7560574.7185.0-0.019101The Immortal Life of Henrietta LacksBiographies & Memoirs
92986I got goose bumps, stopped doing what I was doing, almost cried and laughed while readinglistening to her..will continue with Eat, Pray Love now..This sums it up (my favorite part!)"Surely something wonderful is sheltered inside you. I say this with all confidence, because I happen to believe we are all walking repositories of buried treasure. I believe this is one of the oldest and most generous tricks the universe plays on us human beings, both for its own amusement and for ours The universe buries strange jewels deep within us all, and then stands back to see if we can find them.The hunt to uncover those jewels thats creative living.The courage to go on that hunt in the first place thats what separates a mundane existence from a more enchanted one.The often surprising results of that hunt thats what I call Big Magic."2017-08-1254.7267340My first Elizabeth Gilbert book and I am in LOVE!2,093,039,571,247,340,000200,232,477,616,181,0001,779,336,351,234,390,000101432024-12-052672.00.39.1672.260.6697130.3209364.7115.00.124445Big MagicHealth & Wellness
92987Its an awesome book ive seen the movies first, now making my way to the booksThe book definitely gives more information.2023-06-0254.91967120I love Harry Potter!183,575,103,781,660,000,000154,488,132,403,44316,221,281,441,206,200,0004212024-12-05552.00.114.1158.620.6165970.3768324.7075.00.109673Harry Potter and the Sorcerers Stone Book 1Children's Audiobooks
92988Seriously, I don't know who thought this was such a good book. Jake Gyllenhaal was "okay" as a narrator, but he didn't really wow me with the performance, although I'm not entirely sure if that was his fault. There just really isn't anything "wowing" about this story. \n\nThe entire thing is a narration from the "main character" Nick Carraway, but even then, the entire book is about the real main character Jay Gatsby. That being said, the entire story of the real main character could have been summed up in a matter of 10 pages or less, so the story drags along with unneeded details of Nick's daily activities in order to fill time. Even then, this story (if it can be called that) had no story to tell. It's about a regular guy who really did nothing, went nowhere, and was himself just a facsimile of a dream we all aspire to. Trying to make poetry out of this "story" is just another facsimile.2020-10-2124.5232650What a waste of time.1,503,452,242,102,560,0001,467,039,471,297,340,0001,313,643,142,045,570,00051652024-12-051506.02.511.1567.490.5250790.3485862.1092.00.131710The Great GatsbyLiterature & Fiction